Abstract A quality perspective in data resource management is critical. Because users have different criteria for determining the quality of data, we propose tagging data at the cell level with quality indicators, which are objective characteristics of the data and its manufacturing process. Based on these indicators, the user may assess the data's quality for the intended application. This paper investigates how such quality indicators may be specified, stored, retrieved, and processed. We propose an attribute-based data model, query algebra, and integrity rules that facilitate cell-level tagging as well as the processing of application data that is augmented with quality indicators. An ER-based data quality requirements analysis methodology is proposed for specification of the kinds of quality indicator to be modeled.
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